Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
  • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
  • G06V40/12—Fingerprints or palmprints
  • G06V40/1365—Matching; Classification
  • G06V40/1376—Matching features related to ridge properties or fingerprint texture
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F18/00—Pattern recognition

Definitions

• the present invention relates to a fingerprint recognition method.
• a fingerprint is a swirling ridge formed by protrusion of the sweat glands.
• a protruded swirling portion is called a ridge and a recessed swirling portion is called valley.
• the fingerprint is unique to each person and is not changed all his/her life.
• the fingerprint recognition method has been used as an effective personal identification method since it has relatively high reliability, stability and process speed compared with other recognition methods such as retina, iris, and face recognition methods.
• a typical fingerprint recognition system has a fingerprint input sensor for capturing a fingerprint image, a feature point extracting unit for extracting feature points from the fingerprint image, and a comparison unit for determining if the fingerprint is identical to a reference fingerprint by comparing the feature points with those of the reference fingerprint.
• a typical fingerprint recognition process includes a feature point extraction process and a feature point adjustment process.
• a thinning method is used as the feature point extraction process. That is, relatively broad lines in the ridge pattern of the fingerprint image are thinned to reduce a measuring amount and effectively analyze.
• the thinning method is classified into a repeated pixel removing method and a non-repeated pixel removing method.
• the former is to thin the fingerprint image by consecutively removing unnecessary pixels in the overall finger print image, a relatively large amount of memory capacity is required and the process time is retarded. That is, it takes a long time to identify a user.
• the latter is a fingerprint ridge tracking method that can be done without large amount of memory capacity. Therefore, since a relatively large amount of information can be used, the latter is estimated to be superior to the former in terms of the recognition speed and noise removal.
• noise is removed from a fingerprint image inputted through a fingerprint input system and a pretreatment process for adjusting the fingerprint image whose noise is removed to a predetermined gray level value is done. Then, feature points such as an ending point and a bifurcation point are extracted by tracking the ridge flow in the gray level image.
• Both of the repeated pixel removing method and the non-repeated pixel removing method are designed to recognize the fingerprint based on the feature points of the fingerprint. Therefore, when accurate feature points cannot be obtained from the fingerprint inputted, the accurate fingerprint recognition cannot be realized.
• FlGs. 1 and 2 show examples that may cause fingerprint recognition errors when the fingerprint recognition is performed based on the feature points of the fingerprint.
• FlG. 1 shows a case when the inputted fingerprint is damaged or the fingerprint is unstably inputted.
• his/her inputted fingerprint may be determined as a fingerprint that is not identical to a his/her registered fingerprint.
• FlG. 2 shows a case when the number of feature points of the fingerprint, which can be extracted from the inputted fingerprint, is reduced since the inputted fingerprint corresponds a part of the overall fingerprint. In this case, since the number of feature points that can be compared with the registered genuine fingerprint information is too small, a rejection rate of the right person is increased.
• the feature-point-based fingerprint recognition method has a limitation in accurately recognizing a fingerprint since it is difficult to extract accurate feature points from the fingerprint when the fingerprint is damaged, the fingerprint input is unstable, the inputted fingerprint corresponds only a part of the overall fingerprint, the ridges of the fingerprint are cut by the user's pressure pattern, or the like.
• the present invention is directed to a fingerprint recognition method that substantially obviates one or more problems due to limitations and disadvantages of the related art.
• An object of the present invention is to provide a fingerprint recognition method that can accurately perform the fingerprint recognition even for fingerprint information on damaged fingerprints or partly inputted fingerprint.
• a fingerprint recognition method including: extracting ridge information of an inputted fingerprint; and determining an identity between the inputted fingerprint and a reference fingerprint by comparing the ridge information of the inputted fingerprint with the ridge information of the reference fingerprint using distance variations between adjacent ridges.
• a fingerprint recognition method including: inputting a fingerprint; pre-treating the inputted fingerprint by extracting ridge information from the inputted fingerprint and storing the extracted ridge information; and matching the inputted fingerprint with a reference fingerprint to determine if the inputted fingerprint is identical to the reference fingerprint from the ridge information of the inputted fingerprint according to a distance variation between the inputted fingerprint and the reference fingerprint.
• the mean value of the first differential values of the distances between the referent fingerprint and the inputted fingerprint at the sample location is used. Therefore, even when there is a slight shift or rotation between the inputted fingerprint and the reference fingerprint, since the calculated Score is low when the inputted fingerprint is identical to the reference fingerprint, the identity between the inputted fingerprint and the reference fingerprint can be effectively detected.
• FlGs. 1 and 2 are views illustrating examples that may cause fingerprint recognition errors when the fingerprint recognition is performed based on feature points of a fingerprint;
• FlG. 3 is a flowchart illustrating a pretreatment process for extracting feature points of a fingerprint in a fingerprint recognition method according to an embodiment of the present invention;
• FlG. 4 is a flowchart illustrating a matching process for matching an inputted fingerprint with a reference fingerprint in a fingerprint recognition method according to an embodiment of the present invention;
• FlG. 5 is a view illustrating a fingerprint recognition result when an identical fingerprint is erroneously aligned in a fingerprint recognition method according to an embodiment of the present invention;
• FlG. 6 is a view illustrating a fingerprint recognition result when there is a displacement in an identical fingerprint in a fingerprint recognition method according to an embodiment of the present invention
• FlG. 7 is a view illustrating a fingerprint recognition result between a reference fingerprint and a different fingerprint in a fingerprint recognition method according to an embodiment of the present invention
• FlG. 8 is a view illustrating a fingerprint recognition result for a reference fingerprint and a different fingerprint in a fingerprint recognition method according to an embodiment of the present invention.
• FlG. 3 is a flowchart illustrating a pretreatment process for extracting feature points of a fingerprint in a fingerprint recognition method according to an embodiment of the present invention.
• fingerprint ridge information is extracted from a fingerprint image and stored. This will be now described in more detail.
• S210 When a fingerprint image is inputted (S210), an orientation of the fingerprint is determined to obtain information required for recognizing the fingerprint from the inputted fingerprint image (S220).
• S220 inputted fingerprint image
• the inputted fingerprint image is divided into a plurality of blocks each having a predetermined size and the ridge information on each block is detected.
• a starting point for tracking the ridge is first determined (S230).
• the ridges are tracked and the ridge information is stored.
• a ridge tracking is performed from the starting point of each block (S240).
• the tracking is performed for all of the ridges extending from the starting point of the selected block.
• All of the ridges extending from the starting point are found by tracking a subject ridge using the starting point found in the subject block and location information, orientation information, and information required for the number of ending points of the ridge, a coordinate value of the ending points, bifurcation points of the ridge, and a coordinate value of the bifurcation.
• former tracking information is used to determine a next tracking point.
• the fingerprint image is sampled in a direction perpendicular to the tracking direction. An actual ridge location is found from edge information of the sampled imaged.
• the ridge tracking for the subject block is finished, it is determined if the ridge tracking is finished for the all of the blocks of the inputted fingerprint image
• FIG. 5 is a view illustrating a fingerprint recognition result when an identical fingerprint is erroneously aligned in a fingerprint recognition method according to an embodiment of the present invention.
• the reference fingerprint means a fingerprint that is registered or stored in advance and the inputted fingerprint means a fingerprint that is newly inputted to be compared with the reference fingerprint.
• a matching process between the reference fingerprint and the inputted fingerprint is performed by aligning the reference fingerprint and the inputted fingerprint and overlapping the same one another (S310) and (S320).
• a ridge of a predetermined portion of the inputted fingerprint is sampled (S330).
• S340 a distance from a sampled location of the inputted fingerprint and a ridge of the reference fingerprint, which is closest to the sampled location. That is, a distance from a first ridge of the inputted fingerprint to a first ridge of the reference fingerprint, which is closest to the first ridge of the inputted fingerprint, is calculated.
• the inputted fingerprint has a coordinate value that is detected during the sampling process S330.
• the distanced calculated by the above process is not the accurate value. To solve this problem, there is a need to compare the flows of the ridges of the fingerprints with each other.
• a distance variation from the inputted fingerprint to the reference fingerprint is calculated using the distance between the inputted fingerprint and the reference fingerprint.
• the distance variation is used as a first differential value (S350).
• a mean value of the first differential values is used as a fingerprint determining reference of the person himself/herself and another person. Therefore, the mean value of the first differential values is calculated (S343).
• FlGs. 6 through 8 shows an example for determining identity between the inputted fingerprint and the reference fingerprint.
• the reference characters 'a' and 'b' respectively indicate a ridge of the inputted fingerprint and a ridge of the reference fingerprint.
• Score is low, it is determined that the reference fingerprint and the inputted fingerprint are recognized as a fingerprint of an identical person.
• Score is high, it is determined that the reference fingerprint and the inputted fingerprint are recognized as fingerprints of different persons.
• FlG. 6 is a view illustrating a fingerprint recognition result when there is a displacement in an identical fingerprint in a fingerprint recognition method according to an embodiment of the present invention.
• FIG. 7 is a view illustrating a fingerprint recognition result between a reference fingerprint and a different fingerprint in a fingerprint recognition method according to an embodiment of the present invention.
• FIG. 8 is a view illustrating a fingerprint recognition result for a reference fingerprint and a different fingerprint in a fingerprint recognition method according to an embodiment of the present invention.
• the mean value of the first differential values of the distances between the referent fingerprint and the inputted fingerprint at the sample location is used. Therefore, even when there is a slight shift or rotation between the inputted fingerprint and the reference fingerprint, since the calculated Score is low when the inputted fingerprint is identical to the reference fingerprint, the identity between the inputted fingerprint and the reference fingerprint can be effectively detected.
• the fingerprint ridge-based recognition method even when the feature points are damaged or the number of the feature points is small, the identity between the inputted fingerprint and the reference fingerprint can be effectively determined. [82] Accordingly, when the fingerprint recognition method of the present invention is applied to fingerprint recognition products, the recognition rate can be improved.
• An embodiment of the present invention is applicable to personal information security.

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• Human Computer Interaction (AREA)
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• General Engineering & Computer Science (AREA)
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• Life Sciences & Earth Sciences (AREA)
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• Artificial Intelligence (AREA)
• Collating Specific Patterns (AREA)

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• 2004
  • 2004-12-09 KR KR1020040103312A patent/KR100647362B1/ko not_active IP Right Cessation
• 2005
  • 2005-12-09 US US11/298,036 patent/US20060126908A1/en not_active Abandoned
  • 2005-12-09 WO PCT/KR2005/004224 patent/WO2006062378A1/en active Application Filing
  • 2005-12-09 EP EP05822060A patent/EP1831819A4/en not_active Withdrawn

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